_base_ = [
    "../_base_/models/retinanet_r50_fpn.py",
    "../_base_/datasets/coco_detection.py",
    "../_base_/default_runtime.py",
]
cudnn_benchmark = True
norm_cfg = dict(type="BN", requires_grad=True)
model = dict(
    pretrained="torchvision://resnet50",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=norm_cfg,
        norm_eval=False,
        style="pytorch",
    ),
    neck=dict(relu_before_extra_convs=True, no_norm_on_lateral=True, norm_cfg=norm_cfg),
    bbox_head=dict(type="RetinaSepBNHead", num_ins=5, norm_cfg=norm_cfg),
    # training and testing settings
    train_cfg=dict(assigner=dict(neg_iou_thr=0.5)),
)
# dataset settings
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="Resize", img_scale=(640, 640), ratio_range=(0.8, 1.2), keep_ratio=True),
    dict(type="RandomCrop", crop_size=(640, 640)),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size=(640, 640)),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(640, 640),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=64),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    samples_per_gpu=8,
    workers_per_gpu=4,
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
# optimizer
optimizer = dict(
    type="SGD",
    lr=0.08,
    momentum=0.9,
    weight_decay=0.0001,
    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True),
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy="step", warmup="linear", warmup_iters=1000, warmup_ratio=0.1, step=[30, 40]
)
# runtime settings
runner = dict(type="EpochBasedRunner", max_epochs=50)
